AI News, What is a hacker&#039;s approach to learning machine learning and computer vision fast without much math?

What is a hacker&#039;s approach to learning machine learning and computer vision fast without much math?

Broadly there are three possible categories: (1) Straightforward and a potentially limited system : If you just wish to implement a very straightforward system (train a standard CNN on a given dataset, do SVM classification, do basic segmentation, face detection, etc.) on a platform that provides enough resources for the algorithms to run, and no one is going to ever ask you to improve the performance, you do not need to learn any math, and thus no background behind ML and Vision Algorithms.

For instance, if one asks you that he needs you to improve the performance on a given dataset, you might then need to use bias-variance trade-off concepts, concepts of empirical risk minimization and true error (under an expected sense), understanding why an algorithm works, and inferring from the mathematics of the algorithm what is missing inside.

Also one should try to always analyse ML and vision algorithms from the following perspectives : (a) where the algorithms can be robust and why (b) under what conditions will the algorithms fail and why (c) what is the computational cost of running the algorithm and is it worth the trade off between computational efficiency and slightly improved (not perceivable enough in an application) performance.